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PS 26180HARDWAREDisaster ManagementHeavy R&D

A field-deployable AI-powered Smart Farming Assistant that helps farmers detect crop diseases, pests, nutrient deficiencies, and irrigation needs at an early stage, while improving resilience against droughts, floods, heat waves, and other agricultural risks common in India. The solution should enable higher yields, lower input costs, more efficient water usage, and faster response to emerging threats through real-time on-device intelligence.

Qualcomm IncQualcomm Inc
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30-Second Plain English Summary

Smallholder farmers in India lose 25-40% of crop yields to unpredictable pest infestations, incorrect fertilizer application, and water stress, while existing agritech apps fail because they require constant high-speed cloud internet that is unavailable in rural fields. Build a Field-Deployable AI Smart Farming Assistant for Qualcomm Inc running 100% on-device on Qualcomm Snapdragon mobile platforms that performs instant offline crop disease diagnosis, soil nutrient recommendations, and vernacular voice advisories.

5-Dimension Strategic ScorecardOverall Score: 4.3 / 5.0
Innovation
4.6 / 5
36h Feasibility
3.7 / 5
Uniqueness
4.1 / 5
Jury Appeal
4.8 / 5
Tech Depth
4.4 / 5
Recommended System Architecture Pipeline
Farmer Smartphone Camera & Mic -> Qualcomm Snapdragon Hexagon NPU (QNN/SNPE) -> On-Device Crop Disease & Speech AI -> Local Offline Agronomy DB -> Spoken Voice Audio Output
Hardware Bill of Materials (BOM) & Cost BreakdownEstimated component unit economics in Indian Rupee (INR)
Prototype Unit Cost:16,330
ComponentSpecificationQtyEst. Cost
Raspberry Pi Zero 2 W with Google Coral Edge TPU Mini-PCIeUltra-compact AI coprocessor running MobileNetV3 for 80ms on-device crop disease classification16,200
Sony IMX219 8MP Macro Focus Camera Probe with Ring LEDClose-up leaf lesion, pest infestation, and chlorophyll discoloration imaging sensor11,850
5-in-1 Soil NPK, Moisture, Temperature, & pH Modbus RS485 ProbeStainless steel multi-depth soil probe for real-time fertility and irrigation optimization13,400
SX1262 LoRa 868MHz 5km Farm-to-Home Telemetry NodeTransmits irrigation triggers and disease alerts across extensive rural farmland1780
15W Weatherproof Solar Panel with 7.4V 4400mAh Li-ion Battery KitDelivers all-weather autonomous power in off-grid agricultural fields12,900
IP67 Polycarbonate Pole-Mount Enclosure with Swivel Camera ArmUV-stabilized outdoor casing designed to withstand heavy tropical monsoons and high heat11,200
Power Input: 7.4V DC Solar-Charged Battery System
Form Factor: Field Pole-Mount Weatherproof Agritech Pod with Articulated Camera Head
Architecture & Prototyping Strategy: Two-tier presentation strategy: (1) Hackathon MVP (~₹3k–₹4.5k) with ESP32-CAM + capacitive soil moisture sensor + smartphone app; (2) Smart Agritech Station (~₹16.33k) with Coral TPU AI leaf disease classifier, 5-in-1 NPK/moisture Modbus probe, SX1262 LoRa, and 15W solar panel.
Recommended Tech StackClick to search similar
Official Government Problem Description
Background Agriculture remains a primary livelihood for millions of people in India, but farmers face recurring challenges from droughts, erratic rainfall, floods, pest infestations, crop diseases, heat stress, and soil degradation. Climate variability is increasing the frequency of these risks, affecting crop productivity and farm incomes. Many small and marginal farmers lack access to timely diagnostics and expert advice, particularly in regions with limited internet connectivity. Environmental monitoring, edge AI, and local sensing technologies can help deliver real-time insights directly at the farm level without relying on continuous cloud access.Early detection of crop stress, pest outbreaks, irrigation issues, and adverse environmental conditions can significantly reduce crop losses and improve resilience against agricultural disasters such as droughts, floods, and disease outbreaks. Description Develop a Smart Farming Assistant, an edge AI-powered solution that continuously monitors crop health and environmental conditions directly in the field. Using a combination of cameras, environmental sensors, and on-device AI,the system should identify crop diseases, pest infestations, nutrient deficiencies,water stress, and irrigation requirements in real time.The solution should operate locally on edge devices deployed in farms, enabling rapid analysis and recommendations even in areas with poor connectivity. The system should help farmers make informed decisions about irrigation, pesticide application, fertilizer usage, and crop protection while minimizing water consumption and input costs.The platform should provide actionable alerts and recommendations through a simple mobile or field display interface, enabling farmers to respond quickly to emerging risks before they become large-scale crop failures. Edge AI enables realtime decision-making while reducing dependence on cloud infrastructure. Expected Solution The proposed solution should implement some or all of the following: 1. Crop Health Monitoring • Detect visible signs of crop diseases from leaf and plant images. • Identify nutrient deficiencies through color, texture, and growth analysis. • Monitor crop growth stages and overall field health. 2. Pest Detection and Early Warning • Detect common insect pests and infestation patterns using camera-based AI. • Generate early alerts before infestations spread across fields. • Support targeted intervention rather than blanket pesticide application. 3. Smart Irrigation Management • Monitor soil moisture, temperature, humidity, and weather conditions. • Detect water stress and over-irrigation scenarios. • Recommend optimal irrigation schedules to conserve water. 4. Environmental Risk Monitoring • Track conditions associated with drought, excessive rainfall, flooding, heat stress, and disease outbreaks. • Identify abnormal environmental patterns affecting crop productivity. • Provide localized field-level alerts. 5. Edge AI Processing • Perform image analysis and sensor data processing directly on the device. • Operate in remote areas with limited or intermittent connectivity. • Deliver low-latency recommendations and alerts. 6. Farmer Advisory System • Provide simple recommendations such as: o Irrigate now / delay irrigation o Possible disease detected o Pest activity increasing o Heat-stress warning o Flood-risk alert • Deliver advice through a mobile app, local display, or SMS notifications. 7. Farm Analytics Dashboard • Historical trends in crop health and environmental conditions. • Field-level performance monitoring. • Yield-risk forecasting and decision-support insights. 8. Scalable Deployment • Suitable for smallholder farms, cooperatives, and large agricultural enterprises. • Support integration with weather data, farm equipment, and irrigation systems.
AI & PPT Citation Format

Smart India Hackathon 2026 Problem Statement PS-26180: "A field-deployable AI-powered Smart Farming Assistant that helps farmers detect crop diseases, pests, nutrient deficiencies, and irrigation needs at an early stage, while improving resilience against droughts, floods, heat waves, and other agricultural risks common in India. The solution should enable higher yields, lower input costs, more efficient water usage, and faster response to emerging threats through real-time on-device intelligence.", Ministry: Qualcomm Inc. Strategy & Architecture via SIH ONE (https://sihone.pages.dev/ps/26180)

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